Correlated Mutations and Homologous Recombination Within Bacterial Populations

• Published in: Genetics (Austin) 2017-02, Vol.205 (2), p.891-917
• Main Authors: Lin, Mingzhi, Kussell, Edo
• Format: Article
• Language: English
• Subjects: Acquisitions & mergers; Algorithms; Bacteria; Bias; Computer applications; Computer simulation; Computing time; Correlation analysis; Datasets; Deoxyribonucleic acid; DNA; E coli; Efficiency; Escherichia coli - genetics; Evolution, Molecular; Genetics; Genome, Bacterial; Genomes; Homologous Recombination; Homology; Investigations; Mathematical models; Microorganisms; Models, Genetic; Mutation; Nucleotide sequence; Phylogenetics; Phylogeny; Population; Population genetics; Populations; Probability; Random variables; Selection, Genetic; Streptococcus infections; Streptococcus pneumoniae - genetics; Trees
• ISSN: 1943-2631; 0016-6731; 1943-2631
• DOI: 10.1534/genetics.116.189621

Inferring the rate of homologous recombination within a bacterial population remains a key challenge in quantifying the basic parameters of bacterial evolution. Due to the high sequence similarity within a clonal population, and unique aspects of bacterial DNA transfer processes, detecting recombination events based on phylogenetic reconstruction is often difficult, and estimating recombination rates using coalescent model-based methods is computationally expensive, and often infeasible for large sequencing data sets. Here, we present an efficient solution by introducing a set of mutational correlation functions computed using pairwise sequence comparison, which characterize various facets of bacterial recombination. We provide analytical expressions for these functions, which precisely recapitulate simulation results of neutral and adapting populations under different coalescent models. We used these to fit correlation functions measured at synonymous substitutions using whole-genome data on Escherichia coli and Streptococcus pneumoniae populations. We calculated and corrected for the effect of sample selection bias, i.e., the uneven sampling of individuals from natural microbial populations that exists in most datasets. Our method is fast and efficient, and does not employ phylogenetic inference or other computationally intensive numerics. By simply fitting analytical forms to measurements from sequence data, we show that recombination rates can be inferred, and the relative ages of different samples can be estimated. Our approach, which is based on population genetic modeling, is broadly applicable to a wide variety of data, and its computational efficiency makes it particularly attractive for use in the analysis of large sequencing datasets.